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configs/restorers/rdn/README.md

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+# Residual Dense Network for Image Super-Resolution
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```bibtex
+@inproceedings{zhang2018residual,
+  title={Residual dense network for image super-resolution},
+  author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={2472--2481},
+  year={2018}
+}
+```
+
+## Results
+
+Evaluated on RGB channels, `scale` pixels in each border are cropped before evaluation.
+
+The metrics are `PSNR / SSIM`.
+
+|                                         Method                                         |       Set5       |      Set14       |      DIV2K       |                                                                                                                   Download                                                                                                                    |
+| :------------------------------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [rdn_x2c64b16_g1_1000k_div2k](/configs/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k.py) | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth?versionId=CAEQJxiBgMC774LGyBciIGU3ZGRkZWM3Y2Y0ZjQ2OTliZTc2NmM5ZWY0MDA1MDU3) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json?versionId=CAEQJxiBgICf04_HyBciIDFkMzBiY2Y2ZDE2ZDQ0ZWE4M2MxMjMwMzdhMzY1ZTUz) |
+| [rdn_x3c64b16_g1_1000k_div2k](/configs/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k.py) | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth?versionId=CAEQJxiBgMC3v9LFyBciIGExYWY0NTI0YWVkODQxZDRiYWNlYjViY2E5MzQ4OTc1) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json?versionId=CAEQJxiBgMCtwtLFyBciIDNmNzZjNTUyYTk0MjQ2OTBiYjJiNDNjMTI0NGZhYmI4) |
+| [rdn_x4c64b16_g1_1000k_div2k](/configs/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k.py) | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth?versionId=CAEQJxiBgICwxdLFyBciIGFlMzVhNTBlOGEyNDQwMGI5OGJjOTJkMDQ1ZDJjOTM2) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json?versionId=CAEQJxiBgIC9xtLFyBciIGQ5YTJhMjY0OTE1YjRiMTQ5OTc5YzQ2MjM4ZGVkZWQ1) |